import pandas as pd
from etl.epc.DataProcessor import construction_age_bounds_map
from backend.onboarders.mappings.property_type import parity_map as property_map
from backend.onboarders.mappings.age_band import party_map as age_band_map
from backend.onboarders.mappings.built_form import parity_map as built_form_map


def check_nulls(data, original_column, mapped_column):
    # We only allow nulls if the oroginal value was null
    null_vals = data[pd.isnull(data[mapped_column])]
    if null_vals.empty:
        return True
    # We make sure all original values were null
    assert pd.isnull(null_vals[original_column]).all(), (
        f"Some values in {mapped_column} were not mapped, but original values were not null"
    )


# Sample input data

data = pd.read_excel(
    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
    "- Data Extracts for Domna.xlsx",
    sheet_name="Sustainability"
)

# We want to map the parity fields to standard EPC references. This will allow us to
# 1) Estimate EPCs, more accurately
# 2) Patch incorrect EPCs with ease
# 3) Indicate already installed measures

# ------------ construction_age_band ------------
# Map to EPC age bands
# def construction_date_to_band(year):
#     if pd.isnull(year):
#         return None
#     # Get the year from the date which is numpy datetime format
#     for label, ranges in construction_age_bounds_map.items():
#         if ranges["l"] <= year <= ranges["u"]:
#             return label
#     raise NotImplementedError("year out of bounds")
#
#
# data["construction_age_band"] = pd.to_datetime(data["Construction Date"]).dt.year.apply(construction_date_to_band)

data["construction_age_band"] = data["Construction Years"].map(age_band_map)

check_nulls(data, "Construction Years", "construction_age_band")

# ------------ property_type ------------
data["property_type"] = data["Type"].map(property_map)

assert pd.isnull(data["property_type"]).sum() == 0, "Some property types were not mapped"

# ------------ built_form ------------
data["built_form"] = data["Attachment"].map(built_form_map)

assert pd.isnull(data["built_form"]).sum() == 0, "Some built forms were not mapped"

# ------------ Wall Construction ------------

data["walls_combined"] = data["Wall Construction"] + "+" + data["Wall Insulation"].fillna("Unknown Insulation")

data["Wall Insulation"].value_counts()
data["Wall Construction"].value_counts()

as_built_map = {
    "Cavity": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "Solid Brick": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "System": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "Timber Frame": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "Sandstone": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "Granite": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
    "Cob": {"insulated_age_bands": [], "partial_insulated_age_bands": []},
}


def map_wall_construction(wall_constuction, wall_insulation, construction_age_band):
    if wall_insulation == "AsBuilt":
        # Deduce based on wall construction and age band
        bands = as_built_map.get(wall_constuction, None)
        if bands is None:
            raise NotImplementedError(f"Wall construction {wall_constuction} not in as built map")

        # We check if the age band is in insulated or partial insulated, and if neither, we assume uninsulated

# Variables we want to map
# 'Org Ref', 'Address 1', 'Address 2', 'Address 3', 'Postcode', 'Type',
#        'Attachment', 'Construction Years', 'Wall Construction',
#        'Wall Insulation', 'Roof Construction', 'Roof Insulation',
#        'Floor Construction', 'Floor Insulation', 'Glazing', 'Heating',
#        'Boiler Efficiency', 'Main Fuel', 'Controls Adequacy', 'UPRN',
#        'Total Floor Area (m2)'